add tests

tests/model/test_lora.py

@@ -18,7 +18,9 @@ from typing import Sequence
 import torch
 from peft import LoraModel, PeftModel
 from transformers import AutoModelForCausalLM
+from trl import AutoModelForCausalLMWithValueHead
 
+from llamafactory.extras.misc import get_current_device
 from llamafactory.hparams import get_infer_args, get_train_args
 from llamafactory.model import load_model, load_tokenizer
 
@@ -27,6 +29,8 @@ TINY_LLAMA = os.environ.get("TINY_LLAMA", "llamafactory/tiny-random-Llama-3")
 
 TINY_LLAMA_ADAPTER = os.environ.get("TINY_LLAMA_ADAPTER", "llamafactory/tiny-random-Llama-3-lora")
 
+TINY_LLAMA_VALUEHEAD = os.environ.get("TINY_LLAMA_VALUEHEAD", "llamafactory/tiny-random-Llama-3-valuehead")
+
 TRAIN_ARGS = {
     "model_name_or_path": TINY_LLAMA,
     "stage": "sft",
@@ -67,10 +71,29 @@ def compare_model(model_a: "torch.nn.Module", model_b: "torch.nn.Module", diff_k
             assert torch.allclose(state_dict_a[name], state_dict_b[name]) is True
 
 
+def test_lora_train_qv_modules():
+    model_args, _, _, finetuning_args, _ = get_train_args({"lora_target": "q_proj,v_proj", **TRAIN_ARGS})
+    tokenizer_module = load_tokenizer(model_args)
+    model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
+
+    linear_modules = set()
+    for name, param in model.named_parameters():
+        if any(module in name for module in ["lora_A", "lora_B"]):
+            linear_modules.add(name.split(".lora_", maxsplit=1)[0].split(".")[-1])
+            assert param.requires_grad is True
+            assert param.dtype == torch.float32
+        else:
+            assert param.requires_grad is False
+            assert param.dtype == torch.float16
+
+    assert linear_modules == {"q_proj", "v_proj"}
+
+
 def test_lora_train_all_modules():
     model_args, _, _, finetuning_args, _ = get_train_args({"lora_target": "all", **TRAIN_ARGS})
     tokenizer_module = load_tokenizer(model_args)
     model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
+
     linear_modules = set()
     for name, param in model.named_parameters():
         if any(module in name for module in ["lora_A", "lora_B"]):
@@ -90,6 +113,7 @@ def test_lora_train_extra_modules():
     )
     tokenizer_module = load_tokenizer(model_args)
     model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
+
     extra_modules = set()
     for name, param in model.named_parameters():
         if any(module in name for module in ["lora_A", "lora_B"]):
@@ -113,7 +137,9 @@ def test_lora_train_old_adapters():
     tokenizer_module = load_tokenizer(model_args)
     model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
 
-    base_model = AutoModelForCausalLM.from_pretrained(TINY_LLAMA, torch_dtype=model.dtype, device_map=model.device)
+    base_model = AutoModelForCausalLM.from_pretrained(
+        TINY_LLAMA, torch_dtype=torch.float16, device_map=get_current_device()
+    )
     ref_model = PeftModel.from_pretrained(base_model, TINY_LLAMA_ADAPTER, is_trainable=True)
     for param in filter(lambda p: p.requires_grad, ref_model.parameters()):
         param.data = param.data.to(torch.float32)
@@ -128,7 +154,9 @@ def test_lora_train_new_adapters():
     tokenizer_module = load_tokenizer(model_args)
     model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True)
 
-    base_model = AutoModelForCausalLM.from_pretrained(TINY_LLAMA, torch_dtype=model.dtype, device_map=model.device)
+    base_model = AutoModelForCausalLM.from_pretrained(
+        TINY_LLAMA, torch_dtype=torch.float16, device_map=get_current_device()
+    )
     ref_model = PeftModel.from_pretrained(base_model, TINY_LLAMA_ADAPTER, is_trainable=True)
     for param in filter(lambda p: p.requires_grad, ref_model.parameters()):
         param.data = param.data.to(torch.float32)
@@ -138,17 +166,31 @@ def test_lora_train_new_adapters():
     )
 
 
+def test_lora_train_valuehead():
+    model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
+    tokenizer_module = load_tokenizer(model_args)
+    model = load_model(
+        tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=True, add_valuehead=True
+    )
+
+    ref_model: "AutoModelForCausalLMWithValueHead" = AutoModelForCausalLMWithValueHead.from_pretrained(
+        TINY_LLAMA_VALUEHEAD, torch_dtype=torch.float16, device_map=get_current_device()
+    )
+    state_dict = model.state_dict()
+    ref_state_dict = ref_model.state_dict()
+
+    assert torch.allclose(state_dict["v_head.summary.weight"], ref_state_dict["v_head.summary.weight"])
+    assert torch.allclose(state_dict["v_head.summary.bias"], ref_state_dict["v_head.summary.bias"])
+
+
 def test_lora_inference():
     model_args, _, finetuning_args, _ = get_infer_args(INFER_ARGS)
     tokenizer_module = load_tokenizer(model_args)
     model = load_model(tokenizer_module["tokenizer"], model_args, finetuning_args, is_trainable=False)
 
-    base_model = AutoModelForCausalLM.from_pretrained(TINY_LLAMA, torch_dtype=model.dtype, device_map=model.device)
+    base_model = AutoModelForCausalLM.from_pretrained(
+        TINY_LLAMA, torch_dtype=torch.float16, device_map=get_current_device()
+    )
     ref_model: "LoraModel" = PeftModel.from_pretrained(base_model, TINY_LLAMA_ADAPTER)
     ref_model = ref_model.merge_and_unload()
     compare_model(model, ref_model)
-
-    for name, param in model.named_parameters():
-        assert param.requires_grad is False
-        assert param.dtype == torch.float16
-        assert "lora" not in name